A data distribution monitoring system based on the Internet of Things

Abstract

The invention relates to the technical field of Internet of Things data, and provides a data distribution monitoring system based on the Internet of Things, comprising a task management module, an indicator analysis module, a processing module and a visualization module; the task management module is used to maintain a distribution task table, and periodically scan the distribution task table through a scheduled task to obtain total data to be distributed and device data to be distributed; the indicator analysis module is used to import the collected distribution indicators into Prometheus monitoring for analysis and prediction; the processing module is used to monitor the distribution data to be processed in the task queue, and when the distribution monitoring system detects that a certain device is congested in distribution, the distribution mode is changed to real-time data synchronization; the visualization module is used to create a custom monitoring dashboard, realize the visual display of an operation and maintenance monitoring large screen, view the distribution monitoring status in real time, ensure the stable operation of the system, and effectively improve the operation and maintenance efficiency and security of the system.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things data technology, and in particular to a data distribution monitoring system based on the Internet of Things. Background Art

[0002] With the development of the Internet of Things and the transformation of the domestic industrial Internet of Things, more and more industrial equipment and sensors are constantly integrated into various links of industrial production to improve production efficiency and reduce production costs. In order to control these IoT devices, a background system is required to communicate with these devices and send the background data to the devices.

[0003] In the IoT device network, data delivery is a very important part. This function maintains the communication and data exchange between the background system and the IoT devices. Some data delivery is time-sensitive, and the sooner it is delivered to the device, the better. However, when there are many IoT devices in the network, data delivery during peak hours may face congestion, which may result in some important data not being delivered to the device in the first place.

[0004] For the huge network of IoT devices, how to monitor the background data delivery and discover problems in time is an urgent problem that the IoT system needs to solve. Therefore, a monitoring method is needed to identify such abnormal situations and take emergency measures. Summary of the invention

[0005] The problem solved by the present invention is that when there are many IoT devices in the network during communication and data exchange between the system and the IoT devices, data delivery during peak hours may face congestion, resulting in some important data not being delivered to the devices in the first place.

[0006] To solve the above problems, the present invention provides a data distribution monitoring system based on the Internet of Things, including a task management module, an indicator analysis module, a processing module and a visualization module;

[0007] The task management module is used to maintain the task list, and periodically scan the task list through the scheduled task to obtain the total data to be sent and the device data to be sent;

[0008] The indicator analysis module is used to import the collected issued indicators into Prometheus monitoring for analysis and prediction;

[0009] The processing module is used to monitor the pending data in the task queue, and when the sending monitoring system detects that a certain device is blocked, the sending mode is changed to real-time data synchronization;

[0010] The visualization module is used to create a custom monitoring dashboard, realize the visualization display of the operation and maintenance monitoring screen, and view the real-time monitoring status of the distributed monitoring.

[0011] Preferably, the task management module includes a task status maintenance unit and a timing scanning unit;

[0012] The task status maintenance unit is used to maintain a task distribution table, each record in the task table identifies the status of a task distribution;

[0013] The timing scanning unit is used to periodically execute the timing tasks, scan the sending task table, count and update the total data to be sent and the device data to be sent.

[0014] Preferably, the task status maintenance unit is used to maintain a task delivery table, and each record in the task table identifies a status of a task delivery including:

[0015] Create a record for each issued task in the issued task table, ensuring that each record contains but is not limited to task ID, device ID, task content and status field information;

[0016] A status field is set in the task record to identify the real-time status of the task, including but not limited to pending, being issued, successfully issued, and failed to issue;

[0017] Update the status field in the task record in real time based on the execution status of the issued task;

[0018] Provides an application programming interface or database view to allow external monitoring systems or operation and maintenance personnel to query task status in real time, supporting real-time monitoring and data analysis needs.

[0019] Preferably, the timing scanning unit is used to periodically execute the timing task, scan the sending task table, and count and update the total data to be sent and the device data to be sent, including:

[0020] Filter all tasks whose status is "to be issued" to count the total amount of data to be issued;

[0021] Group by device ID and count the amount of data to be sent for each device, that is, the data to be sent by the device;

[0022] The statistical data of the total data to be sent and the device data to be sent are updated regularly to ensure that the monitoring system obtains the latest status of the data to be sent.

[0023] Preferably, the indicator analysis module is used to import the collected issued indicators into Prometheus monitoring for analysis and prediction, including:

[0024] Import the data to be sent, the data sent, and the sending rate indicators into Prometheus monitoring through the open HTTP interface according to the data format requirements of Prometheus;

[0025] Map the imported indicators to Prometheus monitoring, including but not limited to mapping the data to be sent to the Gauge type to reflect its increase and decrease characteristics;

[0026] Map the delivered data to the Counter type to reflect its characteristic of only increasing and not decreasing. Map the delivery rate to the Gauge type and use the rate function of Prometheus to obtain the average delivery rate within the preset time window.

[0027] Based on the indicator data collected by Prometheus, trend prediction and anomaly detection of the issued indicators are carried out to support performance optimization and fault prevention of the issued system.

[0028] Preferably, the average sending rate within the preset time window is obtained by the following expression:

[0029] rate(data sent{}[5s])

[0030] The rate function is used to obtain the average rate of change of the monitoring indicator within a given time window. The delivered data {} in the expression represents the name of the monitoring indicator registered in Prometheus, which is used to record the cumulative count of successfully delivered data. [5s] represents the length of the time window, which is 5 seconds here. This expression obtains the average rate of change of the delivered data indicator within the past 5-second time window, that is, the average delivery rate.

[0031] Preferably, when the download monitoring system detects that a certain device is experiencing download congestion, changing the download mode to real-time data synchronization includes:

[0032] Periodically monitor the pending dispatch data in the task queue to identify possible queue accumulation and the resulting dispatch delays;

[0033] Establish monitoring rules. Within N consecutive time windows, if the sending rate is lower than the threshold X and the device's waiting data M exceeds the threshold Y, it is determined that the device is experiencing sending congestion, and the emergency response mechanism is activated.

[0034] Preferably, the monitoring rule is used to set the judgment condition for issuing congestion, and is specifically monitored by the following formula:

[0035]

[0036] Among them, ΔN down Indicates the change in the number of data items successfully sent within the time window T; T indicates the length of the time window, measured in seconds; X and Y are the preset sending rate threshold and the device's threshold for data to be sent, respectively; M indicates the average amount of data to be sent by the device within N consecutive time windows.

[0037] Preferably, the initiating emergency response mechanism includes:

[0038] When device congestion is detected, all pending tasks for the device are canceled and the delivery mode is changed to real-time data synchronization, that is, the device actively pulls the latest full data from the server instead of waiting for the server to push.

[0039] Preferably, the visualization module is used to create a custom monitoring dashboard to achieve a visualized display of the operation and maintenance monitoring screen. The real-time viewing of the monitoring status includes:

[0040] Create a custom monitoring dashboard to achieve visual display of the operation and maintenance monitoring screen;

[0041] Configure the data source and connect it to Prometheus monitoring to ensure that the monitoring dashboard reflects the monitoring status in real time;

[0042] Set up alarms and notifications. When monitoring indicators exceed preset thresholds, warnings are automatically triggered to remind operation and maintenance personnel to respond in time.

[0043] Compared with the prior art, the present invention maintains the task table and uses the scheduled task to periodically scan, efficiently collects the total data to be sent and the device data to be sent, and uses the cache to record the cumulative number of successfully sent data, avoiding the performance bottleneck of directly counting from the task table, and achieving the acquisition of the sending rate;

[0044] In order to monitor the real-time analysis of the indicators of data to be sent and the sending rate, the collected sending indicators are imported into the Prometheus monitoring system. The Gauge and Counter types are used to map the data to be sent, the data that has been sent, and the sending rate. The average sending rate is obtained in real time in combination with the rate function of Prometheus, which improves the response speed and analysis accuracy of the monitoring system.

[0045] When device data delivery congestion is detected, the device's pending delivery tasks can be canceled immediately and switched to real-time data synchronization mode, that is, the device actively pulls the latest full data from the server, effectively solving the delivery delay problem and improving the stability and efficiency of data delivery.

[0046] Creating a custom monitoring dashboard enables visual display of the operation and maintenance monitoring screen and real-time viewing of the monitoring status, greatly facilitating operation and maintenance personnel to monitor the system status and troubleshoot problems.

[0047] Ultimately, by optimizing the data collection and analysis process, introducing an emergency response mechanism, and enhancing visual monitoring capabilities, the performance, stability, and operation and maintenance efficiency of the IoT-based data distribution monitoring system were improved, resulting in more efficient data distribution management and monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Schematic diagram of a data distribution monitoring system based on the Internet of Things in an embodiment of the present invention;

[0049] Figure 2 Schematic diagram of a task management module in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0051] It should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection; it can be directly connected, or indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0052] In the description of this specification, the description with reference to the terms "embodiment", "one embodiment" and "one implementation" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or implementation are included in at least one embodiment or implementation of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or implementation. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or implementations in a suitable manner.

[0053] To solve the above problem, in one embodiment of the present invention, Figure 1 As shown, a data distribution monitoring system based on the Internet of Things includes a task management module 101, an indicator analysis module 102, a processing module 103 and a visualization module 104;

[0054] The task management module 101 is used to maintain the task list, and periodically scan the task list through a scheduled task to obtain the total data to be sent and the device data to be sent;

[0055] The indicator analysis module 102 is used to import the collected issued indicators into Prometheus monitoring for analysis and prediction;

[0056] The processing module 103 is used to monitor the pending data in the task queue, and when the sending monitoring system detects that a device is blocked, the sending mode is changed to real-time data synchronization;

[0057] The visualization module 104 is used to create a custom monitoring dashboard, realize the visualization display of the operation and maintenance monitoring screen, and view the real-time status of the monitoring status.

[0058] In one embodiment of the present invention, Figure 2 As shown, in order to collect the sent indicators, the task management module 101 includes a task status maintenance unit 1011 and a timing scanning unit 1012, which ensures the real-time maintenance of the sent task status and the periodic statistics of the data to be sent; the task status maintenance unit 1011 is specifically implemented as follows:

[0059] Create task records: Create detailed records for each task in the task list, ensuring that the records include but are not limited to the following key information:

[0060] Task ID: A number that uniquely identifies each task issued, making it easier to track and manage;

[0061] Device ID: The unique identifier of the target device that is associated with the task being issued, ensuring accurate task allocation;

[0062] Task content: Describe in detail the specific content or parameters of the task to be issued, including but not limited to the data packet size and priority;

[0063] Status field: identifies the real-time status of the task, ensuring that the task management module 101 can understand the progress of the task in a timely manner.

[0064] Set status field: Set up a status field in the task record to identify the real-time status of the task, covering the entire life cycle from task creation to task completion, including but not limited to:

[0065] To be issued: The task has been created but has not yet been issued;

[0066] Issuing: The task is being executed and the data is being transmitted;

[0067] Successful delivery: The task has been completed and the data has been successfully delivered to the target device;

[0068] Delivery failed: An error occurred during task execution and delivery failed.

[0069] Real-time update of status fields: Dynamically update status fields based on the actual execution status of issued tasks to ensure the real-time and accuracy of task status and support dynamic management and scheduling of tasks.

[0070] Provide query interface: Design and implement application programming interface or database view to allow external monitoring system or operation and maintenance personnel to query task status in real time in a safe and efficient way, supporting real-time monitoring and data analysis requirements, including:

[0071] Real-time status query: Provides an interface to query the real-time status of tasks, including but not limited to the current processing progress, the amount of data sent, etc.

[0072] Historical status query: supports querying the historical status of tasks, including the time of issuance, completion time, reasons for failure of issuance, etc., to facilitate post-analysis and auditing;

[0073] Batch status query: supports querying the status of multiple tasks at the same time to meet the needs of large-scale monitoring.

[0074] Finally, the implementation details of the task status maintenance unit 1011 are realized, ensuring the fine management and real-time monitoring of the task status, and providing a basis for the efficient operation of the entire data distribution monitoring system.

[0075] The timing scanning unit 1012 is specifically implemented as follows:

[0076] Periodically filter tasks that are waiting to be issued: Set a scheduled task to automatically scan the issued task table at a predetermined time interval, such as every minute or every five minutes, and filter out all tasks that are waiting to be issued, ensuring that the set of tasks to be issued is captured in real time, providing a basis for subsequent data statistics and analysis;

[0077] Count the total amount of data to be sent: During each scan, count the total number of all filtered tasks to be sent, and use this as the real-time statistical value of the total amount of data to be sent. This statistical result reflects the current load of pending tasks in the system, which helps to evaluate the system processing capacity and resource allocation;

[0078] Group statistics by device ID: Based on the statistics of the total amount of data to be sent, the tasks to be sent are further grouped according to the device ID to obtain the amount of data to be sent for each device, that is, the device data to be sent. This group statistics can help the monitoring system quickly identify which devices have a high amount of tasks to be sent, providing data support for targeted optimization strategies;

[0079] Update statistical results regularly: After completing each scan and statistics, the scheduled scanning unit 1012 will update the statistical results of the total data to be sent and the device data to be sent, ensuring that the monitoring system can obtain the latest data status in real time; this update mechanism ensures the timeliness of the monitoring data, allowing the operation and maintenance personnel and the monitoring system to respond to changes in the system status in a timely manner and take necessary adjustment measures.

[0080] Finally, the timing scanning unit 1012 ensures the periodic monitoring of the status of the issued tasks and the real-time updating of the data, providing key decision support information for the entire monitoring system.

[0081] In one embodiment of the present invention, in order to analyze the sent indicators, the indicator analysis module 102 imports the collected sent indicators into Prometheus monitoring for analysis and prediction in the following manner, specifically including:

[0082] Index import and mapping: First, import the data to be sent, the data sent, and the sending rate sending indicators into Prometheus monitoring through the open HTTP interface according to the data format requirements of Prometheus; then, map the imported indicators to the monitoring indicator types of Prometheus, as follows:

[0083] The data to be sent is mapped to the Gauge type, which reflects its characteristics of increasing and decreasing with the change of task status, and can reflect the dynamic changes of tasks to be sent in the system in real time;

[0084] The delivered data is mapped to the Counter type, reflecting its characteristic of only increasing but not decreasing. It records the total amount of data that has been successfully delivered in the system, providing a cumulative data basis for obtaining the delivery rate.

[0085] The delivery rate is mapped to the Gauge type. Combined with the rate function of Prometheus, the average delivery rate within the preset time window is calculated to reflect the efficiency of data delivery per unit time.

[0086] Calculation of delivery rate: Use the rate function of Prometheus to calculate the average delivery rate. The specific expression is rate(delivered data {}[5s]); where delivered data {} represents the name of the monitoring indicator registered in Prometheus, which is used to record the cumulative count of successfully delivered data; [5s] represents the length of the time window, which is 5 seconds here. This expression calculates the average change rate of the delivered data indicator in the past 5-second time window, that is, the average delivery rate, which provides a real-time data delivery efficiency indicator for the monitoring system.

[0087] Trend prediction and anomaly detection: Based on the indicator data collected by Prometheus, trend prediction and anomaly detection of the issued indicators are carried out to support performance optimization and fault prevention of the issued system; by analyzing historical data, the trend of future data issuance can be predicted and system resources can be planned in advance; at the same time, by setting anomaly detection rules, abnormal situations in the data issuance process can be discovered and responded to in a timely manner to ensure stable operation of the system.

[0088] Finally, the indicator analysis module 102 imported the collected dispatch indicators into the Prometheus monitoring system for analysis and prediction, ensuring the effective use of monitoring data and continuous optimization of the dispatch system performance; through real-time monitoring, trend prediction and anomaly detection, it provided comprehensive data analysis capabilities for the IoT-based data dispatch monitoring system, thereby improving the system's intelligent operation and maintenance level.

[0089] In one embodiment of the present invention, in order to avoid congestion when sending device data, the processing module 103 avoids congestion when sending device data in the following manners, specifically including:

[0090] Periodic monitoring and congestion identification: First, by periodically monitoring the pending dispatch data in the task queue, possible queue accumulation and the resulting dispatch delay can be identified; thereby monitoring the status of dispatched tasks in real time to ensure that the system can respond to abnormal situations in a timely manner.

[0091] Monitoring rule setting and congestion determination: formulate monitoring rules to set the determination conditions for congestion, and monitor specifically through the following formula:

[0092]

[0093] Among them, ΔN down Indicates the change in the number of data successfully sent within the time window T; T indicates the length of the time window in seconds; X and Y are the preset sending rate threshold and the device data threshold to be sent respectively; M indicates the average amount of data to be sent by the device in N consecutive time windows; when the above conditions are met, it is determined that the device is congested in sending, that is, the sending rate is lower than the preset threshold and the amount of data to be sent by the device exceeds the preset threshold;

[0094] Emergency response mechanism is activated: When device congestion is detected, the emergency response mechanism is immediately activated, including canceling all pending tasks of the device and changing the delivery mode to real-time data synchronization; the device will actively pull the latest full data from the server instead of waiting for the server to push; thereby quickly resolving the delivery delay problem, ensuring the timeliness and completeness of data delivery, while reducing the system burden and preventing further deterioration of congestion.

[0095] Finally, the processing module 103 is implemented to effectively avoid congestion when sending device data through periodic monitoring, monitoring rule setting and emergency response mechanism; thereby, through real-time monitoring, accurate judgment and rapid response, the smoothness of data sending and the stability of system operation are ensured, thereby improving the performance and user experience of the data sending monitoring system based on the Internet of Things.

[0096] In one embodiment of the present invention, in order to realize the large-screen display of the monitoring, the visualization module 104 realizes the visualization display of the operation and maintenance monitoring large screen by the following methods, specifically including:

[0097] Customized monitoring dashboard creation: Integrate Grafana components to create highly customized monitoring dashboards to achieve visual display of the operation and maintenance monitoring screen. This dashboard can intuitively display key monitoring indicators, such as data to be sent, sending rate, and sent data. It provides real-time viewing of the sent monitoring status through a graphical interface, enhancing the intuitiveness and ease of use of monitoring;

[0098] Data source configuration and real-time update: Configure Grafana's data source and connect it to Prometheus monitoring to ensure that the monitoring dashboard can reflect the monitoring status in real time. In this way, the latest monitoring data can be pulled from Prometheus and displayed in real time through Grafana's charts, dashboards and other components, so that operation and maintenance personnel can grasp the system operation status at any time and make timely decisions.

[0099] Alarm and notification mechanism: Set alarms and notifications in the monitoring dashboard. When the monitoring indicators exceed the preset thresholds, warnings are automatically triggered, and operation and maintenance personnel are reminded to respond in time via email, SMS or other notification methods. This ensures that when abnormal situations occur, the operation and maintenance team can intervene quickly and take necessary measures to prevent the problem from escalating and ensure the stable operation of the system.

[0100] Finally, the visualization module 104 realizes the solution of large-screen display of monitoring through the creation of custom monitoring dashboards, data source configuration and real-time update, and alarm and notification mechanism; thus, it not only provides an intuitive monitoring view, but also ensures the real-time grasp and response capabilities of operation and maintenance personnel to the system status through real-time updates and alarm notifications, effectively improving the operation and maintenance efficiency and security of the data distribution monitoring system based on the Internet of Things.

[0101] It should be noted that, although the disclosure is disclosed as above, the protection scope of the disclosure is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the disclosure, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. A data distribution monitoring system based on the Internet of Things, characterized in that: It includes task management module, indicator analysis module, processing module and visualization module; The task management module is used to maintain the task list, and periodically scan the task list through the scheduled task to obtain the total data to be sent and the device data to be sent; Group by device ID and count the amount of data to be sent for each device, that is, the data to be sent by the device; The indicator analysis module is used to import the collected issued indicators into Prometheus monitoring for analysis and prediction; The processing module is used to monitor the pending data in the task queue. When the sending monitoring system detects that a device is experiencing sending congestion, the sending mode is changed to real-time data synchronization. Real-time data synchronization means that the device actively pulls the latest full data from the server instead of waiting for the server to push. The visualization module is used to create a custom monitoring dashboard, realize the visualization display of the operation and maintenance monitoring screen, and view the real-time monitoring status of the dispatched monitoring; When the download monitoring system detects that a device is experiencing download congestion, changing the download mode to real-time data synchronization includes: Periodically monitor the pending dispatch data in the task queue to identify queue accumulation and the resulting dispatch delays; Formulate monitoring rules. Within N consecutive time windows, if the sending rate is lower than the threshold X and the device's waiting data M exceeds the threshold Y, it is determined that the device is experiencing sending congestion, and the emergency response mechanism is activated. The monitoring rule is used to set the judgment conditions for issuing congestion, and is specifically monitored through the following formula: ； in, Indicates the change in the number of data items successfully delivered within the time window T; T indicates the length of the time window, measured in seconds; and They are respectively the preset sending rate threshold and the device data threshold to be sent; Indicates that in continuous The average amount of data to be sent by the device within a time window.

2. According to the data distribution monitoring system based on the Internet of Things according to claim 1, it is characterized in that: The task management module includes a task status maintenance unit and a timing scanning unit; The task status maintenance unit is used to maintain a task distribution table, each record in the task table identifies the status of a task distribution; The timing scanning unit is used to periodically execute the timing tasks, scan the sending task table, count and update the total data to be sent and the device data to be sent.

3. The data distribution monitoring system based on the Internet of Things according to claim 2 is characterized in that: The task status maintenance unit is used to maintain the task table, and each record in the task table identifies the status of a task to be issued, including: Create a record for each issued task in the issued task table, ensuring that each record contains but is not limited to task ID, device ID, task content and status field information; A status field is set in the task record to identify the real-time status of the task, including but not limited to pending, being issued, successfully issued, and failed to issue; Update the status field in the task record in real time based on the execution status of the issued task; Provides an application programming interface or database view to allow external monitoring systems or operation and maintenance personnel to query task status in real time, supporting real-time monitoring and data analysis needs.

4. The data distribution monitoring system based on the Internet of Things according to claim 2 is characterized in that: The timing scanning unit is used to periodically execute the timing task, scan the sending task table, count and update the total data to be sent and the device data to be sent, including: Filter all tasks whose status is "to be issued" to count the total amount of data to be issued; The statistical data of the total data to be sent and the device data to be sent are updated regularly to ensure that the monitoring system obtains the latest status of the data to be sent.

5. The data distribution monitoring system based on the Internet of Things according to claim 1 is characterized in that: The indicator analysis module is used to import the collected issued indicators into Prometheus monitoring for analysis and prediction, including: Import the data to be sent, the data sent, and the sending rate indicators into Prometheus monitoring through the open HTTP interface according to the data format requirements of Prometheus; Map the imported indicators to Prometheus monitoring, including but not limited to mapping the data to be sent to the Gauge type to reflect its increase and decrease characteristics; Map the delivered data to the Counter type to reflect its characteristic of only increasing and not decreasing. Map the delivery rate to the Gauge type and use the rate function of Prometheus to obtain the average delivery rate within the preset time window. Based on the indicator data collected by Prometheus, trend prediction and anomaly detection of the issued indicators are carried out to support performance optimization and fault prevention of the issued system.

6. The data distribution monitoring system based on the Internet of Things according to claim 5 is characterized in that: The average sending rate within the preset time window is obtained by the following expression: rate(data sent{}[5s]); The rate function is used to obtain the average rate of change of the monitoring indicator within a given time window. The delivered data {} in the expression represents the name of the monitoring indicator registered in Prometheus, which is used to record the cumulative count of successfully delivered data. [5s] represents the length of the time window, which is 5 seconds here. This expression obtains the average rate of change of the delivered data indicator within the past 5-second time window, that is, the average delivery rate.

7. The data distribution monitoring system based on the Internet of Things according to claim 1 is characterized in that: The initiation of the emergency response mechanism includes: When it is detected that the device is congested in sending data, all tasks to be sent to the device are canceled and the sending mode is changed to real-time data synchronization.

8. The data distribution monitoring system based on the Internet of Things according to claim 1 is characterized in that: The visualization module is used to create a custom monitoring dashboard to achieve a visual display of the operation and maintenance monitoring screen. The real-time viewing of the monitoring status includes: Create a custom monitoring dashboard to achieve visual display of the operation and maintenance monitoring screen; Configure the data source and connect it to Prometheus monitoring to ensure that the monitoring dashboard reflects the monitoring status in real time; Set up alarms and notifications. When monitoring indicators exceed preset thresholds, warnings are automatically triggered to remind operation and maintenance personnel to respond in time.

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